Maintainer - sdukshis
- Neural Networks
- Theano - Python
- TensorFlow - Python, C++
- Lasagne - Python
- Keras - Python
- mxnet - Python, C++, R, Scala, Julia, Go, Matlab, js
- Caffe - Python, C++
- cuDNN - C++
- Requests for research
- scikit-learn - Python
- Rodeo - Python
- BigARTM - Python
- Machine Learning
- Neural Networks
- Recurrent neural networks
- Neural Information Processing Systems (NIPS) - site
- International Conference on Learning Representations (ICLR) - site
- International Conference on Machine Learning (ICML) - site
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Neural Networks
- Feed-forward networks
- Approximation by Superpositions of a Sigmoidal Function, G, Cybenko - Paper
- Deep learning
- Learning multiple layers of representation, Geoffrey Hinton, 2007 - Paper
- Learning Deep Architectures for AT, Yoshua Bengio - Paper
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov - Paper
- Reducing the Dimensionality of Data with Neural Networks, G. E. Hinton* and R. R. Salakhutdinov - Paper
- A theoretical framework for deep transfer learning, T. Galanti, L. Wolf, and T. Hazan - Paper
- Comparative Study of Deep Learning Software Frameworks, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Moh ak Shah - Paper
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy - [Paper](Sergey Ioffe, Christian Szegedy)
- Recurrent Networks
- Long Short-Term Memory, S. Hochreiter, J. Schmidhuber, 1997 - Paper
- Long Short Term Memory Networks for Anomaly Detection in Time Series, P. Malhotra, L. Vig, G. Shroff, P. Agarwal, 2015 - Paper
- A Clockwork RNN, Jan Koutnik, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber, 2014 - Paper
- Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks, Santiago Fernandez, Alex Graves, Jurgen Schmidhuber, 2007 - Paper
- Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber, 2006 - Paper
- Learning Long-Term Dependencies with Gradient Descent is Difficult, Y. Bengio, P. Simard, and P. Frasconi, 1994 - Paper
- Character-Aware Neural Language Models, Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush - Paper
- Leaning longer memory in recurrent neural networks, Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu & Marc’Aurelio Ranzato - Paper
- Recurrent neural network regularization, Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals - Paper
- Learning to forget: continual prediction with LSTM, Felix Gers, Jurgen Schmidhuber, 1999 - Paper
- Unitary Evolution Recurrent Neural Networks, Martin Arjovsky, Amar Shah, Yoshua Bengio, 2015 - Paper
- Visualizing and understanding recurrent networks, Andrej Karpathy, Justin Johnson, Li Fei-Fei - Paper
- Deep Recurrent Neural Networks for Time- Series Prediction, Sharat C. Prasad, Piyush Prasad - Paper
- Synthesis of recurrent neural networks for dynamical system simulation, Adam Trischler, Gabriele MT D'Eleuterio - Paper
- A Recurrent Neural Network for Modelling Dynamical Systems, Coryn A.L. Bailer-Jones , David J.C. MacKay - Paper
- Approximation of Dynamical Time-Variant Systems by Continuous-Time Recurrent Neural Networks, Xiao-Dong Li, John K. L. Ho, and Tommy W. S. Chow - Paper
- Approximation of Discrete-Time State-Space 12.ajectories Using Dynamic Recurrent Neural Networks, Liang Jin, Peter N. Nikiforuk, and Madan M. Gupta - Paper
- Predictive Business Process Monitoring with LSTM Neural Networks, Niek Tax1, Ilya Verenich2,3, Marcello La Rosa2, and Marlon Dumas - Paper
- Convolutional Neural Networks
- Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks, Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, and J Lean Zhao, 2014 - Paper
- Convolutional Networks for Images, Speech, and Time-Series, Yann Lecun, Yoshua Bengio - Paper
- Understanding Convolutional Neural Networks, Jayanth Koushik - Paper
- Deep learning, Yann LeCun, Yoshua Bengio, andGeoffrey Hinton - Paper
- Feed-forward networks
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Time Series Anomaly Detection
- SAX
- LSTM
- LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection, Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, 2016 - Paper
- Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks, Bénard Wiese and Christian Omlin, 2009 - Springer
- Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection, Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu, and Howon Kim - Paper
- Deep Recurrent Neural Network-based Autoencoders for Acoustic Novelty Detection, Erik Marchi Fabio Vesperini, Stefano Squartini, and Bjo ̈rn Schuller - Paper
- A Novel Approach for Automatic Acoustic Novelty Detection Using a Denoising Autoencoder with Bidirectional LSTM Neural Networks, Erik Marchi, Fabio Vesperini, Florian Eyben, Stefano Squartini, Bjo ̈rn Schuller - Paper
- Transfer learning
- Transfer Representation-Learning for Anomaly Detection, Jerone T. A. Andrews, Thomas Tanay, Edward J. Morton, Lewis D. Griffin, 2016 - Paper
- Anomaly Detection Based on Sensor Data in Petroleum Industry Applications, Luis Martí,1, Nayat Sanchez-Pi, José Manuel Molina, and Ana Cristina Bicharra Garcia - Paper
- Anomaly detection in aircraft data using recurrent nueral networks (RNN), Anvardh Nanduri, Lance Sherry - Paper
- Bayesian Online Changepoint Detection, Ryan Prescott Adams, David J.C. MacKay - Paper
- Anomaly Detection in Aviation Data using Extreme Learning Machines, Vijay Manikandan Janakiraman, David Nielsen - Paper
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Clustering
- Consistent Algorithms for Clustering Time Series, Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary, Philippe Preux, 2016 - Paper
- Statistical Language Models based on Neural Networks by Tomas Mikolov
- Time Series Prediction Using Neural Networks by Karol Kuna, 2015
- TRAINING RECURRENT NEURAL NETWORKS by Ilya Sutskever, 2013
- Anomaly Detection of Time Series
- Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks
- Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis - Amazon, Safari
- Fundamentals of Deep Learning, Nikhil Buduma - Safari
- Rank Based Anomaly Detection Algorithms - Book
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Book
- Foundations of Data Science, Avrim Blum, John Hopcroft and Ravindran Kannan - Book
- Journal of Machine Learning Research
- Machine Learning and Data Analysis
- Machine Learning
- International Journal of Machine Learning and Cybernetics
- Data Mining and Knowledge Discovery
- Intelligent Data Analysis
- Интеллектуальные системы
- Artificial Intelligence
- Artificial Intelligence Review
- Engineering Applications of Artificial Intelligence
- PhysioBank
- UCR Time Series Classification Archive
- NASA Shuttle Valve Data
- Yahoo Labeled Anomaly Detection Dataset
- Awesome Public Datasets
- The Numenta Anomaly Benchmark Competition For Real-Time Anomaly Detection
- An archive of datasets distributed with R
- List of datasets for machine learning research
- Disk Defect Data
- Hard Drive Data and Stats
- FMA: A Dataset For Music Analysis
- Estimating Rainfall From Weather Radar Readings Using Recurrent Neural Networks
- Theoretical Motivations for Deep Learning
- CMU Graphics Lab Motion Capture Database
- Common Objects in Context
- Neural Network Architectures
- Building powerful image classification models using very little data
- Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras
- Written Memories: Understanding, Deriving and Extending the LSTM
- Time Series Prediction With Deep Learning in Keras